Accurate and Flexible Malware Detection in Cloud Environments with a Z-Score Based CSOM Model

In order to identify malware in cloud computing settings, this work presents Conjugate Self-Organizing Migration (CSOM), a unique Z-score-based machine learning model. The model's flexibility to changing data distributions is increased by standardizing feature vectors using Z-score normalization. By combining self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the possibility of malware spreading. The imaging technique uses Z-score normalization to assure correct depiction of changing data, presenting the cloud environment as a dynamic, self-organizing landscape. Through conjugate migration approaches, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of minute alterations in harmful activity.The experimental findings demonstrate the effectiveness of the Z-score-based CSOM model in high accuracy malware detection across a range of dynamic cloud settings. The model's outstanding response to emerging cyberthreats is demonstrated by its real-world scenario performance. Because of its improved accuracy and adaptability, the suggested Z-score-based CSOM model ultimately offers a workable solution for cloud computing virus detection. The evolving nature of cloud-based cyber threats may require further research into scalability and refinement.